Transaction risk identification method, device and equipment, medium and product

Abstract

The invention discloses a transaction risk identification method and device, equipment, a medium and a product, and relates to the technical field of big data. The method comprises the following steps: acquiring transaction detail data of all services in a target application, and summarizing the transaction detail data into transaction statistical data according to a preset dimension; analyzing the transaction statistical data to obtain a weight value and a risk coefficient of each service; and determining a product of the weight value and the risk coefficient as a risk assessment value of the corresponding service. According to the technical scheme provided by the invention, the accuracy and efficiency of transaction log anomaly identification can be improved, and main production hidden dangers can be solved with lower cost.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a transaction risk identification method, device, equipment, medium and product. Background Art

[0002] After large-scale cloud migration of applications, the transaction logs of applications are sent to the log center for storage using a unified solution. The log center provides query capabilities to help locate application problems. After the application completes the distributed transformation, the number of application devices increases several times or even dozens of times compared to before the transformation, and the amount of logs generated by the application every day also increases in the same proportion, which puts higher requirements on the operation and maintenance of application logs.

[0003] The existing technology usually combines the log system to identify the risks in failed transactions every day. However, the existing log system stores hundreds of TB of log information. Due to the large number of transaction logs and the presence of both normal logs and error logs of transactions with different risks, it is difficult to quickly discover potential risks through simple error keywords. Summary of the invention

[0004] The present application provides a transaction risk identification method, device, equipment, medium and product, which can improve the accuracy and efficiency of transaction log anomaly identification and solve major production risks at a lower cost.

[0005] In a first aspect, the present application provides a transaction risk identification method, the method comprising:

[0006] Obtain transaction details of all services in the target application, and aggregate the transaction details into transaction statistics according to preset dimensions;

[0007] Analyze the transaction statistics to obtain a weight value and a risk factor for each service;

[0008] The product of the weight value and the risk coefficient is determined as the risk assessment value of the corresponding service.

[0009] Furthermore, the transaction statistics data are analyzed to obtain a weight value for each service, including: obtaining the transaction volume of each service from the transaction statistics data; dividing the services into critical services and non-critical services based on the transaction volume; and configuring corresponding weight values ​​for the critical services and the non-critical services, respectively.

[0010] Furthermore, dividing the services into critical services and non-critical services based on the transaction volume includes: determining the services whose transaction volumes are greater than a preset quantity standard as the critical services; and determining the services other than the critical services as the non-critical services.

[0011] Furthermore, the transaction statistics data are analyzed to obtain a risk coefficient for each service, including: obtaining a return code for each service from the transaction statistics data; calculating a success rate for each service based on the return code for each service; and setting a risk coefficient for a corresponding service based on the success rate of each service.

[0012] Furthermore, setting a risk factor for the corresponding service based on the success rate of each of the services includes: determining a decline in the success rate of each of the services; if the decline exceeds a first percentage, determining the risk factor of the corresponding service as a first value; if the decline exceeds a second percentage but does not exceed the first percentage, determining the risk factor of the corresponding service as a second value; if the decline does not exceed the second percentage, determining the risk factor of the corresponding service as a third value; wherein the first value is greater than the second value, the second value is greater than the third value, and the first percentage is greater than the second percentage.

[0013] Furthermore, the method also includes: when the risk assessment value is greater than a preset value, determining that the service has a potential risk; and determining the error cause corresponding to the potential risk through a preset risk analysis model based on the error keyword of the service.

[0014] In a second aspect, the present application provides a transaction risk identification device, the device comprising:

[0015] A data acquisition module is used to acquire transaction details data of all services in the target application, and aggregate the transaction details data into transaction statistics data according to preset dimensions;

[0016] A data analysis module, used to analyze the transaction statistics data to obtain a weight value and risk factor for each service;

[0017] The risk identification module is used to determine the product of the weight value and the risk coefficient as the risk assessment value of the corresponding service.

[0018] In a third aspect, the present application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the transaction risk identification method described in any embodiment of the present application.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the transaction risk identification method described in any embodiment of the present application when executed.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the transaction risk identification method described in any embodiment of the present application.

[0021] In order to solve the defects of the prior art in the background technology, the embodiment of the present application provides a transaction risk identification method, and the execution of this method can bring the following beneficial effects: the present application analyzes the transaction statistics data of all services, determines the weight values ​​and risk coefficients of different services, and then determines the risk assessment value of each service. The present application can solve the problem that it is difficult to quickly find potential risks through error keywords due to the large number of transaction logs, can solve the pain points of production operation and maintenance, and quickly find production hidden dangers. Compared with the existing transaction log analysis solution, the present application solution improves the accuracy and efficiency of transaction log anomaly identification, and solves the main production hidden dangers at a lower cost.

[0022] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the transaction risk identification device, or may be packaged separately from the processor of the transaction risk identification device, and this application does not limit this.

[0023] The description of the second, third, ..., and fifth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, ..., and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0024] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description.

[0025] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1A flowchart of a transaction risk identification method provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the structure of a transaction risk identification device provided in an embodiment of the present application;

[0029] Figure 3 It is a block diagram of an electronic device used to implement a transaction risk identification method in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0031] It should be noted that the terms "first", "second", "target", and "original" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including", "having", and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0032] Figure 1 A flowchart of a transaction risk identification method provided in an embodiment of the present application is provided. This embodiment can be applied to scenarios where risk identification is performed on application transaction data. A transaction risk identification method provided in this embodiment can be performed by a transaction risk identification device provided in an embodiment of the present application. The device can be implemented in software and / or hardware and integrated in an electronic device that executes the method.

[0033] See also Figure 1 The method of this embodiment includes but is not limited to the following steps:

[0034] S110. Obtain transaction detail data of all services in the target application, and aggregate the transaction detail data into transaction statistics data according to preset dimensions.

[0035] The target application refers to a specific software program or application system, such as an e-commerce application, an online payment platform, a financial service application, etc. It is a carrier that carries various services and conducts transaction activities. All services refer to the various services of different functions and types provided by the application. Taking e-commerce applications as an example, they may include product purchase services, logistics query services, and after-sales services; for financial applications, there may be transfer and remittance services, financial purchase services, and account query services. Each service may generate corresponding transaction activities during the use of users.

[0036] Transaction detail data refers to a data set that records in detail the information related to each transaction. Transaction detail data may include the time when each transaction occurred (accurate to year, month, day, hour, minute, and second), the two parties involved in the transaction (such as account number, name, etc.), transaction amount, transaction type (such as consumption, recharge, refund, etc.), details of goods or services related to the transaction (such as the name, specifications, quantity, etc. of the purchased goods, or the specific content of the services received), transaction order number, payment method and other details.

[0037] Transaction statistics refer to relatively more macro and general data obtained by aggregating transaction details according to preset dimensions, which is the basis for subsequent analysis of each service weight value and risk factor. Unlike transaction details data, transaction statistics mainly present the refined and summarized transaction situation.

[0038] In the embodiment of the present application, the transaction details data can be obtained through the application programming interface (API), directly querying data from the application database based on the table structure and fields storing the transaction details data, or using the data backup or data warehouse to obtain the transaction details data if the application regularly backs up the data. The transaction details data can be summarized according to the dimensions of application + container template + service name / method name + error keyword to obtain relatively more macro and general transaction statistics.

[0039] S120: Analyze the transaction statistics to obtain the weight value and risk factor of each service.

[0040] The weight value is an indicator that measures the relative importance of each service in the overall business. It is calculated based on multiple factors in transaction statistics. The risk factor is an indicator that assesses the degree of risk that each service may face.

[0041] Specifically, the transaction statistics data are analyzed to obtain the weight value of each service, including: obtaining the transaction volume of each service from the transaction statistics data, and counting the transaction volume of all services under the target application; dividing the services into key services and non-key services based on the amount of transaction data; and configuring corresponding weight values ​​for key services and non-key services respectively. For example, the weight value of the key service is set to 2, and the weight value of the non-key service is set to 1.

[0042] Furthermore, services are divided into key services and non-key services based on transaction volume, including: sorting the transaction volume of all services from large to small, determining services with transaction volume greater than a preset quantity standard as key services; and determining services other than key services as non-key services. Optionally, key services and non-key services and their daily transaction volume information are stored separately in a database table, and key services and non-key services are identified in the database using different fields.

[0043] Specifically, the transaction statistics data are analyzed to obtain the risk coefficient of each service, including: the first step is to obtain the return code of each service from the transaction statistics data. The second step is to calculate the success rate of each service based on the return code of each service. The specific process can be: parse the return code of each transaction in an automated manner. If the return code is not a preset default value, then the status of the transaction is considered to be a failure; if the return code is a preset default value, then the status of the transaction is considered to be a success, and the number of successful transactions corresponding to each service can be obtained. For any service, the result of dividing the number of successful transactions by the total number of transactions is used as the success rate of each service. The third step is to set the risk coefficient for the corresponding service based on the success rate of each service.

[0044] In another optional embodiment, the method for calculating the success rate of each service can also be: obtaining the number of calls and the number of failures for each service from the database of the holographic monitoring platform; subtracting the number of failures from the number of calls to obtain the number of successes, and dividing the number of successes by the number of calls as the success rate of each service.

[0045] Furthermore, a risk factor is set for the corresponding service based on the success rate of each service, including: determining the decline in the success rate of each service; if the decline exceeds a first percentage (such as 5%), determining the risk factor of the corresponding service as a first value (such as 1); if the decline exceeds a second percentage (such as 1%) and does not exceed the first percentage, determining the risk factor of the corresponding service as a second value (such as 0.5); if the decline does not exceed the second percentage, determining the risk factor of the corresponding service as a third value (such as 0.1); wherein the first value is greater than the second value, the second value is greater than the third value, the first percentage is greater than the second percentage, and the present application does not specifically limit these values.

[0046] For example, when the change condition is a decrease of 5 percentage points or more, the risk coefficient of the service is determined to be 1; when the change condition is a decrease within 1 to 5 percentage points, the risk coefficient of the service is determined to be 0.5; when the change condition is a decrease within 1 percentage point, the risk coefficient of the service is determined to be 0.1.

[0047] S130. Determine the product of the weight value and the risk coefficient as the risk assessment value of the corresponding service.

[0048] The weight value is used to reflect the relative importance of the service in the overall business, and the risk coefficient is used to reflect the degree of risk that the service may face. These two indicators describe the characteristics of the service from different perspectives. Multiplying them together is a comprehensive evaluation method, the purpose of which is to obtain a single indicator that can consider both the importance and risk level of the service, namely the risk assessment value.

[0049] Furthermore, the transaction risk identification method of the present application also includes: when the risk assessment value is greater than a preset value (such as 1), determining that the service has a potential risk; determining the error cause corresponding to the potential risk through a preset risk analysis model based on the error keyword of the service. When the risk assessment value is not greater than the preset value, it means that the risk of the transaction is general and can be ignored first. Optionally, the risk situation of the failed transaction can be calculated for transactions with different error types.

[0050] The technical solution provided in this embodiment obtains the transaction details data of all services in the target application, aggregates the transaction details data into transaction statistics data according to preset dimensions; analyzes the transaction statistics data to obtain the weight value and risk coefficient of each service; and determines the product of the weight value and the risk coefficient as the risk assessment value of the corresponding service. This application determines the weight values ​​and risk coefficients of different services by analyzing the transaction statistics data of all services, and then determines the risk assessment value of each service. This application can solve the problem that it is difficult to quickly discover potential risks through error keywords due to the large number of transaction logs, can solve the pain points of production operation and maintenance, and quickly discover production hazards. Compared with the existing transaction log analysis solution, this application solution improves the accuracy and efficiency of transaction log anomaly identification, and solves major production hazards at a lower cost.

[0051] Figure 2 A schematic diagram of a transaction risk identification device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the device 200 may include:

[0052] The data acquisition module 210 is used to acquire the transaction details data of all services in the target application, and aggregate the transaction details data into transaction statistics data according to preset dimensions;

[0053] A data analysis module 220 is used to analyze the transaction statistics data to obtain a weight value and a risk factor for each service;

[0054] The risk identification module 230 is used to determine the product of the weight value and the risk coefficient as the risk assessment value of the corresponding service.

[0055] Furthermore, the data analysis module 220 specifically includes: a weight value determination unit and a risk coefficient determination unit;

[0056] The weight value determination unit can be used to obtain the transaction volume of each of the services from the transaction statistics data; divide the services into key services and non-key services based on the transaction volume; and configure corresponding weight values ​​for the key services and the non-key services respectively.

[0057] Furthermore, the weight value determination unit may be specifically configured to determine the service whose transaction volume is greater than a preset quantity standard as the key service; and determine the service other than the key service as the non-key service.

[0058] The risk coefficient determination unit can be used to obtain the return code of each of the services from the transaction statistics data; calculate the success rate of each of the services based on the return code of each of the services; and set the risk coefficient for the corresponding service based on the success rate of each of the services.

[0059] Furthermore, the above-mentioned risk coefficient determination unit can also be used to determine the decline in the success rate of each of the services; if the decline exceeds a first percentage, the risk coefficient of the corresponding service is determined as a first value; if the decline exceeds a second percentage and does not exceed the first percentage, the risk coefficient of the corresponding service is determined as a second value; if the decline does not exceed the second percentage, the risk coefficient of the corresponding service is determined as a third value; wherein the first value is greater than the second value, the second value is greater than the third value, and the first percentage is greater than the second percentage.

[0060] Furthermore, the risk identification device for the above transaction may also include: a cause analysis module;

[0061] The cause analysis module is used to determine that the service has potential risks when the risk assessment value is greater than a preset value; and determine the error cause corresponding to the potential risk through a preset risk analysis model based on the error keyword of the service.

[0062] The transaction risk identification device provided in this embodiment can be applied to the transaction risk identification method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0063] Figure 3 It is a block diagram of an electronic device for implementing a risk identification method of a transaction of an embodiment of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0064] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0065] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0066] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a risk identification method for a transaction.

[0067] In some embodiments, the risk identification method for transactions may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the risk identification method for transactions described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the risk identification method for transactions in any other appropriate manner (e.g., by means of firmware).

[0068] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0069] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0070] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0071] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0072] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server) or a computing system that includes middleware components (e.g., an application server) or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein) or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0073] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0074] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. For example, those skilled in the art can use the various forms of processes shown above to reorder, add, or delete steps; the steps recorded in the present application can be performed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present application can be achieved, and this document does not limit them here.

[0075] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A transaction risk identification method, characterized in that: The method comprises: Obtain transaction details of all services in the target application, and aggregate the transaction details into transaction statistics according to preset dimensions; Analyze the transaction statistics to obtain a weight value and a risk factor for each service; The product of the weight value and the risk coefficient is determined as the risk assessment value of the corresponding service.

2. The transaction risk identification method according to claim 1, characterized in that: The analyzing the transaction statistics data to obtain the weight value of each service includes: Obtaining the transaction volume of each of the services from the transaction statistics; Classifying the services into critical services and non-critical services based on the transaction volume; The corresponding weight values ​​are configured for the critical service and the non-critical service respectively.

3. The transaction risk identification method according to claim 2, characterized in that: The dividing the services into key services and non-key services based on the transaction volume includes: Determine the service whose transaction volume is greater than a preset quantity standard as the key service; Services other than the critical service are determined as the non-critical services.

4. The transaction risk identification method according to claim 1, characterized in that: The transaction statistics data are analyzed to obtain the risk factor of each service, including: Obtaining a return code of each of the services from the transaction statistics data; Calculate the success rate of each of the services based on the return code of each of the services; A risk factor is set for the corresponding service based on the success rate of each of the services.

5. The transaction risk identification method according to claim 4, characterized in that: The risk factor is set for the corresponding service based on the success rate of each service, including: determining a decrease in the success rate of each of said services; If the decline condition exceeds a first percentage, the risk factor of the corresponding service is determined to be a first value; If the decline condition exceeds the second percentage and does not exceed the first percentage, the risk factor of the corresponding service is determined to be a second value; If the decline condition does not exceed the second percentage, determining the risk factor of the corresponding service as a third value; The first value is greater than the second value, the second value is greater than the third value, and the first percentage is greater than the second percentage.

6. The transaction risk identification method according to claim 1, characterized in that: The method further comprises: When the risk assessment value is greater than a preset value, it is determined that the service has a potential risk; Based on the error keywords of the service, the error cause corresponding to the potential risk is determined through a preset risk analysis model.

7. A transaction risk identification device, characterized in that: The device comprises: A data acquisition module is used to acquire transaction details data of all services in the target application, and aggregate the transaction details data into transaction statistics data according to preset dimensions; A data analysis module, used to analyze the transaction statistics data to obtain a weight value and risk factor for each service; The risk identification module is used to determine the product of the weight value and the risk coefficient as the risk assessment value of the corresponding service.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the transaction risk identification method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the transaction risk identification method according to any one of claims 1 to 7 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the transaction risk identification method according to any one of claims 1 to 7.

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